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We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.

This paper describes the repackaging and consistent distribution of the world’s major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use.

Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM’s radiation code is uniquely valuable for GCM evaluation because 1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and 2) the fractional contribution of each band to the LW broadband CRE (fCRE) is sensitive to cloud-top height but largely insensitive to cloud fraction, thereby presenting a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band-by-band quantities from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations. A study is presented here that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs—the GFDL Atmospheric Model version 2 (GFDL AM2), NASA Goddard Earth Observing System version 5 (GEOS-5), and the fourth-generation AGCM of the Canadian Centre for Climate Modelling and Analysis (CCCma CanAM4)—forced by observed SST. The models agree with observation on the annual-mean LW broadband CRE over the tropical oceans within ±1 W m−2. However, the differences among these three GCMs in some bands can be as large as or even larger than ±1 W m−2. Observed seasonal cycles of fCRE in major bands are shown to be consistent with the seasonal cycle of cloud-top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of fCRE agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of fCRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.

The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

What: to celebrate the twentieth anniversary of PCmDi and to honor its founder, w. lawrence gates, more than 100 specialists and leaders in climate modeling met to discuss PCmDi’s history and the future of climate modeling When: 6 april 2009 Where: bethesda, maryland T wenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered Gates’s early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized the scientific ideas that are the basis of PCMDI’s success, summarizing their AFFILIATIONS: Potter—University of California Davis, Davis, California; bader—oak ridge national laboratory, oak ridge, tennessee; riChes and josePh—office of biological and environmental research, U.s. Department of energy, washington, D.C.; baMzai—Climate and large-scale Dynamics Program, national science foundation, arlington, Virginia CORRESPONDING AUTHOR: gerald l. Potter, Department of geology, University of California, Davis, one shields avenue, Davis, Ca 95616 e-mail: jpotter@ucdavis.edu

The Atmospheric Model Intercomparison Project (AMIP), initiated in 1989 under the auspices of the World Climate Research Programme, undertook the systematic validation, diagnosis, and intercomparison of the performance of atmospheric general circulation models. For this purpose all models were required to simulate the evolution of the climate during the decade 1979—88, subject to the observed monthly average temperature and sea ice and a common prescribed atmospheric CO2 concentration and solar constant. By 1995, 31 modeling groups, representing virtually the entire international atmospheric modeling community, had contributed the required standard output of the monthly means of selected statistics. These data have been analyzed by the participating modeling groups, by the Program for Climate Model Diagnosis and Intercomparison, and by the more than two dozen AMIP diagnostic subprojects that have been established to examine specific aspects of the models' performance. Here the analysis and valida...

The ability of the ECMWF model (cycle 33) to simulate the Indian and East Asian summer monsoons is evaluated at four different horizontal resolutions: T21, T42, T63, and T1O6. Generally, with respect to the large-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. However, on regional scales, important differences among the high-frequency temporal variability serve as a further critical rest of the model's ability to simulate the monsoon. T106 best captures both the spatial and temporal characteristics of the Indian and East Asian monsoons, whereas T42 fails to correctly simulate the sequence and development of synoptic-scale milestones that characterize the monsoon flow. In particular, T106 is superior at simulating the development and migration of the monsoon trough over the Bay of Bengal. In the T42 simulation, the development of the monsoon occurs one month earlier than typically observed. At this time the trough is incorrectly locate...

Cloud radiative forcing constitutes the radiative impact of clouds upon the earth's present climate, while cloud radiative feedback is the change in this forcing associated with climatic change. The present study addresses two issues concerning cloud radiative forcing. The first is that an intercomparison of cloud radiative forcing, as predicted by six different general circulation models, shows a considerable lack of agreement, underscoring the need for an improved understanding of cloud/radiation interactions within such models. The second issue pertains to an examination of strategies by which model-predicted cloud/radiation interactions should be compared with satellite-derived data sets. DOI: 10.1111/j.1600-0870.1987.tb00321.x

Two datasets have been combined to demonstrate how the availability of more comprehensive datasets could serve to elucidate the shortwave radiative impact of clouds on both the atmospheric column and the surface. These datasets consist of two measurements of net downward shortwave radiation: one of near-surface measurements made at the Boulder Atmospheric Observatory tower, and the other of collocated top-of-the-atmosphere measurements from the Earth Radiation Budget Experiment. Output from the European Centre for Medium Range Weather Forecasts General Circulation Model also has been used as an aid in interpreting the data, while the data have in turn been employed to validate the model's shortwave radiation code as it pertains to cloud radiation properties. Combined, the datasets and model demonstrate a strategy for determining under what conditions the shortwave radiative impact of clouds leads to a heating or cooling of the atmospheric column. The datasets also show, in terms of a linear slope-offset algorithm for retrieving the net downward shortwave radiation at the surface from satellite measurements, that the clouds present during this study produced a modest negative bias in the retrieved surface flux relative to that inferred from a clear-sky algorithm.

[1] We compare the balance of terms in moisture and temperature prediction equations during short forecasts by the Community Atmosphere Model (CAM2) with observed estimates at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site for two intensive observing periods (IOPs). The goal is to provide insight into parameterization errors which ultimately should lead to model improvements. The atmospheric initial conditions are obtained from high-resolution numerical weather prediction (NWP) analyses. The land initial conditions are spun up to be consistent with those analyses. Three cases are considered: (1) June/July 1997 when the atmosphere is relatively moist and surface evaporation corresponds to 90% of the precipitation with advection accounting for the remainder; (2) rainy days in April 1997 when the atmosphere is less moist and horizontal advection accounts for much of the precipitation with a small contribution from surface evaporation and the balance being derived from the water already present in the column; and (3) nonrainy days of the April 1997 when the moist process parameterizations are inactive and the planetary boundary layer (PBL) parameterization is dominant. For the first case the Zhang-McFarlane deep convective parameterization drives the model to a wrong state. For the second the Hack shallow convective parameterization appears to be not acting deep enough. During both periods inconsistencies between CAM2 and ARM surface fluxes, land surface conditions and the net surface radiative fluxes indicate that the exchange parameterizations should be examined further. For the third case the PBL parameterization does not appear to create the correct vertical structure. In addition, the individual components of the dynamical tendency are very different between CAM2 and ARM, although the total dynamical tendency is similar in the two. Although these observations do not imply that those components are themselves wrong since they may be responding to other errors, each of these components should be examined further to determine the cause of their behaviors.

[1] The Community Atmospheric Model 2 (CAM2) is run as a short-term (1–5 days) forecast model initialized with reanalysis data. The intent is to reveal model deficiencies before complex interactions obscure the root error sources. Integrations are carried out for three Atmospheric Radiation Measurement (ARM) Program intensive operational periods (IOPs): June/July 1997, April 1997, and March 2000. The ARM data are used to validate the model in detail for the Southern Great Plains (SGP) site for all the periods and in the tropical west Pacific for the March 2000 period. The model errors establish themselves quickly, and within 3 days the model has evolved into a state distinctly different from the ARM observations. The summer forecasts evince a systematic error in convective rainfall. This error manifests itself in the temperature and moisture profiles after a single diurnal cycle. The same error characteristics are seen in the March 2000 tropical west Pacific forecasts. The model performs well in the spring cases at the SGP. Most of the error is manifested during rainy periods. The ARM cloud radar comparison to the model reveals cloud errors which are consistent with the relative humidity profile errors. The cloud errors are similar to those seen in climatological integrations, but the state variable errors are different. Thus there is the possibility that the some basic parameterization errors are obscured in the climatological integrations. The approach described here will facilitate parameterization experimentation, diagnoses, and validation. One way of reducing cloud feedback uncertainty is to make the physical processes behave in the most realistic manner possible. Paradoxically, perhaps the best way to reduce uncertainty in cloud feedback mechanisms is to evaluate the model processes with realistic forcing before such feedbacks have any significant affect.

[1] This study implements a revised convective triggering condition in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, Version 2 (CAM2), model to reduce its excessive warm season daytime precipitation over land. The new triggering mechanism introduces a simple dynamic constraint on the initiation of convection that emulates the collective effects of lower level moistening and upward motion of the large-scale circulation. It requires a positive contribution from the large-scale advection of temperature and moisture to the existing positive convective available potential energy (CAPE) for model convection to start. In contrast, the original convection triggering function in CAM2 assumes that convection is triggered whenever there is positive CAPE, which results in too frequent warm season convection over land arising from strong diurnal variation of solar radiation. We examine the impact of the new trigger on CAM2 simulations by running the climate model in numerical weather prediction (NWP) mode so that more available observations and high-frequency NWP analysis data can be used to evaluate model performance. We show that the modified triggering mechanism has led to considerable improvements in the simulation of precipitation, temperature, moisture, clouds, radiations, surface temperature, and surface sensible and latent heat fluxes when compared to the data collected from the Atmospheric Radiation Measurement (ARM) Program at its Southern Great Plains (SGP) site. Similar improvements are also seen over other parts of the globe. In particular, the surface precipitation simulation has been significantly improved over both the continental United States and around the globe; the overestimation of high clouds in the equatorial tropics has been substantially reduced; and the temperature, moisture, and zonal wind are more realistically simulated. Results from this study also show that some systematic errors in the CAM2 climate simulations can be detected in the early stage of model integration. Examples are the extremely overestimated high clouds in the tropics in the vicinity of Intertropical Convergence Zone and the spurious precipitation maximum to the east of the Rockies. This has important implications in studies of these model errors since running the climate model in NWP mode allows us to perform a more in-depth analysis during a short time period where more observations are available and different model errors from various processes have not compensated for the systematic errors.

[1] We compare cloud-radiative forcing (CRF) at the top-of-the atmosphere from 19 atmospheric general circulation models, employing simulations with prescribed sea-surface temperatures, to observations from the Earth Radiation Budget Experiment (ERBE). With respect to 60°N to 60°S means, a surprising result is that many of the 19 models produce unusually large biases in Net CRF that are all of the same sign (negative), meaning that many of the models significantly overestimate cloud radiative cooling. The primary focus of this study, however, is to demonstrate a diagnostic procedure, using ERBE data, to test if a model might produce, for a given region, reasonable CRF as a consequence of compensating errors caused either by unrealistic cloud vertical structure, cloud optical depth or cloud fraction. For this purpose we have chosen two regions, one in the western tropical Pacific characterized by high clouds spanning the range from thin cirrus to deep convective clouds, and the other in the southeastern Pacific characterized by trade cumulus. For a subset of eight models, it is found that most typically produce more realistic regionally-averaged CRF (and its longwave and shortwave components) for the southeastern region as opposed to the western region. However, when the diagnostic procedure for investigating cloud vertical structure and cloud optical depth is imposed, this somewhat better agreement in the southeastern region is found to be the result of compensating errors in either cloud vertical structure, cloud optical depth or cloud fraction. The comparison with ERBE data also shows large errors in clear-sky fluxes for many of the models.

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements.

Snow feedbacks produced by 14 atmospheric general circulation models have been analyzed through idealized numerical experiments. Included in the analysis is an investigation of the surface energy budgets of the models. Negative or weak positive snow feedbacks occurred in some of the models, while others produced strong positive snow feedbacks. These feedbacks are due not only to melting snow, but also to increases in boundary temperature, changes in air temperature, changes in water vapor, and changes in cloudiness. As a result, the net response of each model is quite complex. We analyze in detail the responses of one model with a strong positive snow feedback and another with a weak negative snow feedback. Some of the models include a temperature dependence of the snow albedo, and this has significantly affected the results.

A clear-sky flux computation method is described which is representative of the Earth Radiation Budget Experiment data processing, while at the same time being enough straightforward for implementation in a general circulation model (GCM). The method is a hybrid version of Cess and Potter (1987) Method I and Method II clear-sky top-of-the-atmosphere flux computations for GCMs. The procedure is demonstrated using the ECMWF GCM.

Responses of the surface energy budgets and hydrologic cycles of 19 atmospheric general circulation models to an imposed, globally uniform sea surface temperature perturbation of 4 K were analyzed. The responses of the simulated surface energy budgets are extremely diverse and are closely linked to the responses of the simulated hydrologic cycles. The response of the net surface energy flux is not controlled by cloud effects; instead, it is determined primarily by the response of the latent heat flux. The prescribed warming of the oceans leads to major increases in the atmospheric water vapor content and the rates of evaporation and precipitation. The increased water vapor amount drastically increases the downwelling IR radiation at the earth's surface, but the amount of the change varies dramatically from one model to another.

Extended (180-day) high resolution (T106) perpetual January and July integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) model have been analyzed in terms of the spatial and temporal characteristics of the model's convective activity in the tropics. The model's outgoing longwave radiation (OLR) is used as a surrogate for convective activity, consistent with similar studies based on satellite observations. The 3 hourly temporal sampling is sufficient to allow diagnosis of intradiurnal and interdiurnal variability; the length of the integrations is adequate for identifying lower-frequency, intraseasonal phenomena. Wherever possible, use is made of results from surface or satellite observations of the temporal characteristics of convection to verify the model results. At intradiurnal time scales the model captures the amplitude and phase of the diurnal harmonic over both land and sea. The largest amplitudes occur over the summer continents, with contrasting phases of maximum OLR depending on the presence of convective activity. Over the oceans the model shows a coherent structure to the diurnal cycle associated with regions of convection. Analysis of synoptic (2 to 10 days) and low-frequency (greater than 10 days) variability shows that in many instances the model agrees well with observations. For both seasons the modelmore » simulates westward moving phenomena over the oceans, whose phase speed is reasonable. In July these easterly waves display well-defined periodicities, in agreement with observations, while in January they are more episodic. Low-frequency variability is more prevalent in January, particularly over the convectively active regions of the eastern hemisphere. In general, this variability has a larger spatial scale than the synoptic variability; its periodicities, some in excess of 30 days, are typical of intraseasonal time scales. 56 refs., 10 figs.« less

Radiation fields from a perpetual July integration of a T106 version of the ECMWF operational model are used to identify the most appropriate way to diagnose cloud radiative forcing in a general circulation model, for the purposes of intercomparison between models. Differences between the methods 1 and 2 of Cess and Potter (1987) and a variant method are addressed. Method 1 is shown to be the least robust of all methods, due to the potential uncertainties related to persistent cloudiness, length of the sampling period, and biases in retrieved clear sky quantities due to insufficient sampling of the diurnal cycle. Method 2 is proposed as an unambiguous way to produce consistent radiative diagnostics for intercomparing model results. The impact of the three methods on the derived sensitivites and cloud feedbacks following an imposed change in sea surface temperature is discussed. The sensitivity of the results to horizontal resolution is considered by using the diagnostics from parallel integrations with T21 version of the model. 20 refs., 2 figs., 1 tab.

A method is described for analyzing the feedback and synergistic contributions of temperature, water vapor, cloud cover, surface albedo and CO2 to the change in the radiation balance at the top of the atmosphere due to a perturbation in an annual-averaged zonal atmospheric climate model. The method is illustrated through analysis of a doubled CO2 experiment with the Lawrence Livermore. National Laboratory Statistical Dynamical Model (LLNL SDM). The method provides insight into the sensitivity of the model to feedback changes in individual parameters and how each parameter influences the effects of the others.

An atmospheric radiation model is used here to illustrate several features associated with modeling the diurnal cycle of the planetary albedo. It is found that even for clear regions there appear to be deficiencies in our knowledge of how to model this quantity. The diurnal amplitude factor, defined as the ratio of the diurnally averaged planetary albedo to that at noon, between two GCMs and measurements made from a geostationary satellite. While reasonable consistency is found, the comparisons underscore difficulties associated with converting local-time albedo measurements, as made from sun-synchronous satellites, to diurnally averaged albedos.

In this paper we examine and clarify several arguments that have been put forth by Dr. S. B. Idso in an article appearing within this issue of Climatic Change.

The sensitivity of outgoing longwave flux to changes in cloud cover (∂F/∂Ac) as defined by Cess (1976) must be evaluated carefully to avoid discrepancies arising from the interchange of averaging conventions. In a recent zonal atmospheric model experiment the global value of ∂F/∂Ac was different in sign than in other calculations. This difference in behavior was traced to a latitudinal redistribution of cloud amount and height that occurred in the doubled CO2 experiment. However, when ∂F/∂Ac was evaluated at individual latitudes and then weighted globally, the value of this parameter was consistent with those found by Cess (1976) and Budyko (1974).

Charney's (1975) suggestion that high desert albedo initiates a biogeophysical positive feedback response by the atmosphere has been tested in the 2-D zonal atmospheric model, ZAM2. The results agree with the earlier test in the NASA GISS 3-D GCM model (Charney et al. 1975) in confirming a positive feedback through precipitation but indicate a negative feedback in terms of temperature. The latter suggests that tropical deserts may play a role in air conditioning or placing an upper-bound on the temperature of our planet.

In order to better identify and more fully understand the differences in sensitivity among climate models, two quite different models are systematically compared in terms of their seasonal response. The two-dimensional statistical dynamical model (SDM) developed at the Lawrence Livermore National Laboratory and the Oregon State University three-dimensional general circulation model (GCM) were integrated using as closely comparable boundary conditions and forcing as possible. Comparison of the seasonal anomaly (defined as the departure of the monthly zonal average from the zonal annual mean at each latitude) shows that the models agree quite well in terms of the seasonal phase and amplitude of net radiation simulated at the top of the atmosphere, the tropospheric average temperature and surface temperature and the precipitation. The models also resemble the observed seasonal anomalies of these variables to a reasonable degree, although there are significant errors in each formulation.

A previously determined model [9], based on the theoretical knowledge of the physical modifications of solar energy by the atmosphere and its transformation at the surface, has been used to determine the monthly distribution of net radiation in southern California at a topoclimatic scale. Parameters utilized in the equations were cloud cover, cloud type, surface albedo, precipitable water vapor, surface temperature, slope angle, slope orientation, elevation, and urban sinog. A remarkable contrast was found between relatively high net radiation values for the littoral and coastal mountains and those of the lower values of the arid eastern parts of southern California.

Suggestions have persisted over the past few years that, contary to conventional estimates, increasing levels of atmospheric carbon dioxide will produce negligible warming of the earth's surface, or might even result in surface cooling. In the present paper we reexamine several aspects of these suggestions and illustrate that they are either founded in various violations of the first law of thermodynamics, or that they are based upon misinterpretations of historical data.

The present study provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. This intercomparison uses sea surface temperature change as a surrogate for climate change. The interpretation of cloud-climate interactions is given special attention. A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models' depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors. It is further emphazied that cloud feedback is the consequence of all interacting physical and dynamical processes in a general circulation model. The result of these processes is to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiative response termed cloud feedback.

We compare seasonal changes in cloud-radiative forcing (CRF) at the top of the atmosphere from 18 atmospheric general circulation models, and observations from the Earth Radiation Budget Experiment (ERBE). To enhance the CRF signal and suppress interannual variability, we consider only zonal mean quantities for which the extreme months (January and July), as well as the northern and southern hemispheres, have been differenced. Since seasonal variations of the shortwave component of CRF are caused by seasonal changes in both cloudiness and solar irradiance, the latter was removed. In the ERBE data, seasonal changes in CRF are driven primarily by changes in cloud amount. The same conclusion applies to the models. The shortwave component of seasonal CRF is a measure of changes in cloud amount at all altitudes, while the longwave component is more a measure of upper level clouds. Thus important insights into seasonal cloud amount variations of the models have been obtained by comparing both components, as generated by the models, with the satellite data. For example, in 10 of the 18 models the seasonal oscillations of zonal cloud patterns extend too far poleward by one latitudinal grid.

Two climate models, a one-dimensional radiative-convective model and a seasonal statistical-dynamical model, have been used to obtain a qualitative understanding of climate forcing mechanisms and feedback processes associated with the climatic impact of carbonaceous Arctic aerosols. The models are consistent in suggesting that such aerosols should produce surface warming in Arctic regions, but the manner in which this is accomplished is a bit unusual. Since the aerosols appear in a region and season for which the atmosphere exhibits strong static stability, aerosol-induced changes in the surface radiation budget would be expected to govern the change in surface climate. Although the direct impact of the aerosol is to reduce absorbed solar radiation at the surface, this effect is minimized by the high surface albedos, which in turn, due to the large surface reflection, enhance aerosol solar absorption. This aerosol-induced atmospheric heating then results in increased infrared emission from the atmosphere to the surface that more than compensates for the reduced surface solar absorption, thus producing surface warming. The seasonal statistical-dynamical model further exhibits interesting cryospheric feedback processes, while suggesting springtime Arctic warming that is roughly consistent in timing with observed trends in high latitude temperatures, an effect some have attributed to CO2-induced changes.

To evaluate the possible influence of natural background tropospheric aerosols upon the earth's present climate, we have incorporated aerosol radiation models for continental and maritime aerosols into the Lawrence Livermore National Laboratory statistical-dynamical climate model. The model results suggest that background tropospheric aerosols produce 3°–4°C global surface cooling, with maximum cooling occurring at high latitudes, results which are essentially consistent with an energy balance climate model study by Coakley et al. (1983). To specifically delineate effects caused directly by the aerosols, as opposed to indirect effects resulting from aerosol-induced climate change, a second climate perturbation was considered that consisted of reducing the solar constant so as to give exactly the same initial reduction in surface-atmosphere solar absorption as for the inclusion of tropospheric aerosols. These separate climate perturbations produced nearly identical climate feedback effects, together with similar changes in atmospheric stability and hydrological cycle, despite the fact that the two perturbations have quite different latitudinal and vertical distributions. This finding is consistent with a general circulation model study by Manabe and Wetherald (1980) concerning perturbations of both atmospheric CO2 and the solar constant. A related conclusion is that the model's climate response to tropospheric aerosols is insensitive to the manner in which the aerosols are vertically distributed.

We have employed two climate models for the purpose of qualitatively understanding climate forcing mechanisms and feedback processes associated with the injection of atmospheric smoke and dust (i.e., atmospheric perturbations due to a nuclear exchange). One of these models is the Oregon State University general circulation model (GCM), modified through the addition of a delta-Eddington solar radiation routine to accomodate the inclusion of smoke and dust. The second model is a radiative convective model (RCM), which mimics as closely as possible many of the processes portrayed by the GCM. The primary role of the RCM was that of an educational tool used to reveal climate forcing and response processes, which would explain the behavior of the more complex GCM. The RCM served this purpose extremely well. Specific features revealed by the RCM are summarized as follows. (1) Even for very modest smoke loading, convective coupling of the model's surface and troposphere was insufficient to produce conventional surface-troposphere climate forcing. However, this was not the case when the model's climate was changed by increasing the atmospheric carbon dioxide, by increasing the solar constant, or by the inclusion of natural tropospheric aerosols. (2) Because of the above, for smoke injection the model's climate responded to two distinctly different and opposing radiative forcing mechanisms: direct surface-troposphere heating and direct surface cooling. (3) For a progressive increase in smoke loading a transition occurred from dominant (but not governing) surface-troposphere forcing to dominant surface forcing, with this transition being the result of changes both in vertical convection and in infrared radiation incident upon the surface. (4) The above two processes further impacted the nature of the model's climate response to the dual forcing mechanisms, and interactively they produced quite unusual time-dependent behavior. For example, there were situations in which the short-term climate response to a smoke injection was that of surface cooling, whereas the long-term response was one of warming. This understanding of the RCM behavior greatly aided the interpretation of the GCM results under conditions where the smoke loading, the smoke vertical distribution, and the smoke single scattering albedo were both independently and simultaneously varied. The GCM's surface cooling further exhibited a marked dependence upon which day of the control run (a perpetual July) the smoke is injected.

Based upon the need to understand differences between general circulation model projections of climatic change due to increasing atmospheric carbon dioxide, the present study first categorizes reasons for these differences and presents suggestions for the design of future climate model simulations, so that these specific categories may directly be addressed and understood. Following this, and based upon tutorial use of a radiative-convective model, it is suggested that sea surface temperature perturbations may be used, in conjunction with separation of clear and overcast regions within a model, as a surrogate climatic change for the purpose of understanding and intercomparing atmospheric climate feedback processes. This approach is illustrated through use of the Oregon State University/Lawrence Livermore National Laboratory general circulation model, with particular attention being paid to interpreting cloud/climate interactions within the model.